RUL Prediction of Wet Clutches based on Bivariate Inverse Gaussian Degradation Process with Common Random Variable

doi:10.12382/bgxb.2024.1043

Abstract

Abstract:

Wet multi-disc clutch is a critical component within the vehicle’s integrated transmission system，where the accurate online prediction of remaining useful life (RUL) plays a key role in ensuring the operational safety and formulating the condition-based maintenance strategies.A bivariate degradation model with common random variable based on inverse Gaussian processes is developed.The correlation between variables is characterized by a common random variable.The Bayesian method is used for the posterior estimation of model parameters，and the index for goodness-of-fit test is proposed.Considering the operation and maintenance process of wet clutch equipment，the solving method for its dynamic failure rate and availability is derived，and an online RUL prediction method based on Monte Carlo simulation is proposed.The effectiveness of the proposed model is verified with different sample sizes through simulation study and degradation analysis of wet clutch tests.Compared to traditional Gamma models，the proposed bivariate inverse Gaussian process model is enable to accurately estimate the failure rates and availability of wet clutch and complete more precise online RUL prediction.

Key words: wet clutch, degradation analysis, inverse Gaussian process, remaining useful life prediction

FENG Yuqing, ZHENG Changsong, YU Liang, ZHANG Dingge, ZHANG Jinle, ZHANG Yudong. RUL Prediction of Wet Clutches based on Bivariate Inverse Gaussian Degradation Process with Common Random Variable[J]. Acta Armamentarii, 2025, 46(S1): 241043-.

Figures/Tables 13

References 26

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(n，m)	指标	参数
(n，m)	指标	ω	κ	λ₁	λ₂	α₁	α₂
(1，30)	Mean	14.761	10.006	0.309	0.363	1.264	0.827
(1，30)	MSE	3.392	5.977	0.0069	0.0405	0.00275	0.00691
(5，50)	Mean	16.461	10.082	0.280	0.185	1.313	0.927
(5，50)	MSE	0.840	5.962	0.0016	0.0005	0.00028	0.00086
(15，30)	Mean	16.188	10.049	0.273	0.210	1.307	0.908
(15，30)	MSE	0.192	6.223	0.0011	0.0003	0.00015	0.00018
(25，50)	Mean	16.225	9.928	0.285	0.205	1.305	0.907
(25，50)	MSE	0.106	6.265	0.0003	0.0001	0.00004	0.00007

(n，m)	指标	参数
(n，m)	指标	ω	κ	λ₁	λ₂	α₁	α₂
(1，30)	Mean	14.761	10.006	0.309	0.363	1.264	0.827
(1，30)	MSE	3.392	5.977	0.0069	0.0405	0.00275	0.00691
(5，50)	Mean	16.461	10.082	0.280	0.185	1.313	0.927
(5，50)	MSE	0.840	5.962	0.0016	0.0005	0.00028	0.00086
(15，30)	Mean	16.188	10.049	0.273	0.210	1.307	0.908
(15，30)	MSE	0.192	6.223	0.0011	0.0003	0.00015	0.00018
(25，50)	Mean	16.225	9.928	0.285	0.205	1.305	0.907
(25，50)	MSE	0.106	6.265	0.0003	0.0001	0.00004	0.00007

参数	均值	95%置信区间
ω	6.080	(2.775，9.998)
κ	0.091	(0.008，0.185)
λ₁	9.571	(5.707，13.266)
λ₂	1.789	(1.073，2.568)
α₁	0.853	(0.819，0.888)
α₂	0.524	(0.483，0.564)

参数	均值	95%置信区间
ω	6.080	(2.775，9.998)
κ	0.091	(0.008，0.185)
λ₁	9.571	(5.707，13.266)
λ₂	1.789	(1.073，2.568)
α₁	0.853	(0.819，0.888)
α₂	0.524	(0.483，0.564)

设备	退化模型	WAIC	MAE
离合器1	文献[20]方法	455.77	0.02
离合器1	本文方法	444.23	0.0098
离合器2	文献[20]方法	472.74	0.0206
离合器2	本文方法	468.76	0.0049